A dynamic aggregation strategy enhanced efficient global optimization algorithm for solving high-dimensional turbomachinery design problems

To address challenges effectively in turbomachinery design optimization involving high-dimensional $ (d\geq 30) $ ( d ≥ 30 ) expensive black-box problems, a dedicated Efficient Global Optimization (EGO) algorithm is proposed with dynamic aggregation. This specialized approach efficiently navigates o...

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Veröffentlicht in:Engineering optimization Jg. 57; H. 2; S. 514 - 542
Hauptverfasser: Wang, Qineng, Guo, Zhendong, Chen, Yun, Ma, Guangjian, Song, Liming, Li, Jun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Taylor & Francis 01.02.2025
Schlagworte:
High-dimensional black-box problem
knowledge mining
surrogate-based optimization
ISSN:0305-215X, 1029-0273
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Zusammenfassung:To address challenges effectively in turbomachinery design optimization involving high-dimensional $ (d\geq 30) $ ( d ≥ 30 ) expensive black-box problems, a dedicated Efficient Global Optimization (EGO) algorithm is proposed with dynamic aggregation. This specialized approach efficiently navigates optimization tasks with limited sample evaluations. Specifically, the Dynamic Aggregate Efficient Global Optimization (DA-EGO) algorithm decomposes the original high-dimensional design space into low-dimensional subspaces for efficient surrogate-based optimization search, and the optimal solutions of subspaces are combined as an elite-point for the global search. Most importantly, the subspace variables are updated in each iteration, according to the variable interaction analyses in the sub- and full-spaces. The perturbation method and the analysis of variance are used to detect variable interactions. After being validated on 21 benchmark functions ranging from 30 to 90 dimensions, the DA-EGO is used for the optimization of a transonic compressor rotor with 28 variables and a multi-stage compressor optimization with 60 variables. With the above, the effectiveness of the proposed algorithm is well demonstrated.
ISSN:0305-215X
1029-0273
DOI:10.1080/0305215X.2024.2325651